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Electronic Colloquium on Computational Complexity

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REPORTS > AUTHORS > TAL HERMAN:
All reports by Author Tal Herman:

TR25-152 | 20th October 2025
Tal Herman, Guy Rothblum

Proving Natural Distribution Properties is Harder than Testing Them

Suppose that an untrusted analyst claims that it ran a distribution tester and determined that an unknown distribution has a certain property. Can the untrusted analyst prove that its assertion is correct to a verifier that does not have sufficient samples and computational resources to run the tester on its ... more >>>


TR24-141 | 12th September 2024
Tal Herman

Public Coin Interactive Proofs for Label-Invariant Distribution Properties

Assume we are given sample access to an unknown distribution $D$ over a large domain $[N]$. An emerging line of work has demonstrated that many basic quantities relating to the distribution, such as its distance from uniform and its Shannon entropy, despite being hard to approximate through the samples only, ... more >>>


TR24-094 | 19th May 2024
Tal Herman, Guy Rothblum

Interactive Proofs for General Distribution Properties

Suppose Alice has collected a small number of samples from an unknown distribution, and would like to learn about the distribution. Bob, an untrusted data analyst, claims that he ran a sophisticated data analysis on the distribution, and makes assertions about its properties. Can Alice efficiently verify Bob's claims using ... more >>>


TR23-161 | 1st November 2023
Tal Herman, Guy Rothblum

Doubly-Efficient Interactive Proofs for Distribution Properties

Revisions: 1

Suppose we have access to a small number of samples from an unknown distribution, and would like learn facts about the distribution.
An untrusted data server claims to have studied the distribution and makes assertions about its properties. Can the untrusted data server prove that its assertions are approximately correct? ... more >>>


TR22-052 | 18th April 2022
Tal Herman, Guy Rothblum

Verifying The Unseen: Interactive Proofs for Label-Invariant Distribution Properties

Given i.i.d. samples from an unknown distribution over a large domain $[N]$, approximating several basic quantities, including the distribution's support size, its entropy, and its distance from the uniform distribution, requires $\Theta(N / \log N)$ samples [Valiant and Valiant, STOC 2011].

Suppose, however, that we can interact with a powerful ... more >>>




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